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Using Prior Knowledge for Community Detection by Label Propagation Algorithm

机译：通过标签传播算法使用现有知识进行社区检测

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摘要

Community detection is an important approach to analyze and understand the organization or unit structure of the complex networks. By comparing the existing community detection algorithms, the label propagation algorithm (LPA) shows prominent operation speed and qualifies near linear time complexity. However, original LPA algorithm only uses the topological structure to guide the community detection process, failing to improve the quality of community detection when extra information offered. In this paper, we combine the prior information with topological structure to guide the community detection process. During the label propagation process, we proposed a new label update principle, making a node absorb its neighbor label information depending on the label distribution. The experimental results both on real networks and artificial networks show that the improved algorithm not only inherits the characteristic of rapid speed, but also improves the quality of community detection. Moreover, the improved algorithm still has the feature of near linear time complexity.
机译：社区检测是分析和理解复杂网络的组织或单位结构的重要方法。通过比较现有的社区检测算法，标签传播算法（LPA）显示了突出的操作速度，并限定了线性时间复杂度附近。然而，原始LPA算法仅使用拓扑结构来指导社区检测过程，未能提高社区检测质量，何时提供额外信息。在本文中，我们将先前信息与拓扑结构结合起来引导社区检测过程。在标签传播过程中，我们提出了一个新的标签更新原理，使节点根据标签分布而吸收邻居标签信息。实验结果既有实际网络和人工网络则表明，改进的算法不仅继承了快速速度的特征，还提高了社区检测的质量。此外，改进的算法仍然具有近线性时间复杂度的特征。

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